GraphCanon updated 1mo · GitHub synced 1mo
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Decision brief
AutoAudit leverages LLMs specifically for cyber security tasks and supports custom fine-tuning through models such as GPT, LLAMA, LoRA, and QLORA.
Good fit when
- When your project requires a language model focused on cyber security applications rather than general content generation.
- If you plan to use or adapt existing models like GPT for specialized cyber security tasks with the flexibility of fine-tuning options.
Avoid when
- For projects needing broad, general-purpose text generation that does not require cyber security expertise embedded in the model.
- In scenarios where proprietary data privacy is a concern, given AutoAudit's nature as an LLM for cyber security may imply certain data processing policies could be less flexible.
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (511d since push)
- As of 1mo
- Provenance
- Not a fork · Personal account
- As of 1mo
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
git clone https://github.com/ddzipp/AutoAuditSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
AutoAudit is an LLM tailored for cyber security applications with support for fine-tuning using models like GPT, LLAMA, LoRA, and QLORA.
Capability facts
- Languages
- html
Source: github.language · Jul 25, 2026
Categories
Tags
README
Future Plans
- Inspired by CyberPal, we plan to synthesize a high-quality cybersecurity corpus: This dataset will include open/closed book question answering, yes/no questions, multiple-choice Q&A, and Chain of Thoughts (CoT). We aim to open-source both the dataset and the corresponding code, providing a valuable resource for the cybersecurity research community.
- Responding to the current trend of Agents, we will further integrate security tools such as Nmap, Metasploit, etc., and reference agent frameworks like MetaGPT to automate cybersecurity operations as much as possible. This will help streamline security tasks and improve operational efficiency.
- Evaluating the security of cybersecurity-specific large models: We plan to assess the potential security risks associated with these models, such as possible jailbreaks or backdoors. This will ensure that the models remain secure and resilient against adversarial threats in real-world applications.
For agents
This page has a .md twin and JSON over the API.